gtsam::KalmanFilter Class Reference

Detailed Description

Kalman Filter class.

Knows how to maintain a Gaussian density under linear-Gaussian motion and measurement models. It uses the square-root information form, as usual in GTSAM.

The filter is functional, in that it does not have state: you call init() to create an initial state, then predict() and update() that create new states out of an old state.

Public Member Functions
	KalmanFilter (size_t n, Factorization method=KALMANFILTER_DEFAULT_FACTORIZATION)
State	init (const Vector &x0, const SharedDiagonal &P0) const
	Create initial state, i.e., prior density at time k=0 In Kalman Filter notation, these are x_{0|0} and P_{0|0}. More...
State	init (const Vector &x0, const Matrix &P0) const
	version of init with a full covariance matrix
void	print (const std::string &s="") const
	print
State	predict (const State &p, const Matrix &F, const Matrix &B, const Vector &u, const SharedDiagonal &modelQ) const
	Predict the state P(x_{t+1}|Z^t) In Kalman Filter notation, this is x_{t+1|t} and P_{t+1|t} Details and parameters: In a linear Kalman Filter, the motion model is f(x_{t}) = F*x_{t} + B*u_{t} + w where F is the state transition model/matrix, B is the control input model, and w is zero-mean, Gaussian white noise with covariance Q.
State	predictQ (const State &p, const Matrix &F, const Matrix &B, const Vector &u, const Matrix &Q) const
State	predict2 (const State &p, const Matrix &A0, const Matrix &A1, const Vector &b, const SharedDiagonal &model) const
	Predict the state P(x_{t+1}|Z^t) In Kalman Filter notation, this is x_{t+1|t} and P_{t+1|t} After the call, that is the density that can be queried. More...
State	update (const State &p, const Matrix &H, const Vector &z, const SharedDiagonal &model) const
	Update Kalman filter with a measurement For the Kalman Filter, the measurement function, h(x_{t}) = z_{t} will be of the form h(x_{t}) = H*x_{t} + v where H is the observation model/matrix, and v is zero-mean, Gaussian white noise with covariance R. More...
State	updateQ (const State &p, const Matrix &H, const Vector &z, const Matrix &Q) const

Static Public Member Functions
static Key	step (const State &p)
	Return step index k, starts at 0, incremented at each predict.

Public Types
enum	Factorization { QR , CHOLESKY }
	This Kalman filter is a Square-root Information filter The type below allows you to specify the factorization variant.
typedef GaussianDensity::shared_ptr	State
	The Kalman filter state is simply a GaussianDensity.

Member Function Documentation

init()

KalmanFilter::State gtsam::KalmanFilter::init	(	const Vector &	x0,
		const SharedDiagonal &	P0
	)		const

Create initial state, i.e., prior density at time k=0 In Kalman Filter notation, these are x_{0|0} and P_{0|0}.

Parameters

x0	estimate at time 0
P0	covariance at time 0, given as a diagonal Gaussian 'model'

predict2()

KalmanFilter::State gtsam::KalmanFilter::predict2	(	const State &	p,
		const Matrix &	A0,
		const Matrix &	A1,
		const Vector &	b,
		const SharedDiagonal &	model
	)		const

Predict the state P(x_{t+1}|Z^t) In Kalman Filter notation, this is x_{t+1|t} and P_{t+1|t} After the call, that is the density that can be queried.

Details and parameters: This version of predict takes GaussianFactor motion model [A0 A1 b] with an optional noise model.

update()

KalmanFilter::State gtsam::KalmanFilter::update	(	const State &	p,
		const Matrix &	H,
		const Vector &	z,
		const SharedDiagonal &	model
	)		const

Update Kalman filter with a measurement For the Kalman Filter, the measurement function, h(x_{t}) = z_{t} will be of the form h(x_{t}) = H*x_{t} + v where H is the observation model/matrix, and v is zero-mean, Gaussian white noise with covariance R.

In this version, R is restricted to diagonal Gaussians (model parameter)

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The documentation for this class was generated from the following files:

• /Users/dellaert/git/github/gtsam/linear/KalmanFilter.h
• /Users/dellaert/git/github/gtsam/linear/KalmanFilter.cpp